European Radiology最新文献

{"title":"Characterization of hepatocellular carcinoma with CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI.","authors":"Clément Malthiery, Gabriela Hossu, Ahmet Ayav, Valérie Laurent","doi":"10.1007/s00330-024-11314-1","DOIUrl":"10.1007/s00330-024-11314-1","url":null,"abstract":"<p><strong>Objectives: </strong>This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of MR images, along with radiologist confidence.</p><p><strong>Materials and methods: </strong>This prospective single-center trial included patients who underwent four-phase liver CT and multiphasic contrast-enhanced MRI within 7 days from February to August 2023. The lesion characteristics, LI-RADS classifications and confidence scores according to two radiologists on the ASIR, DLR and MRI techniques were compared. If the patient had at least one lesion, he was included in the HCC group, otherwise in the non-HCC group. MRI being the technique with the best sensitivity, concordance of lesions characteristics and LI-RADS classifications were calculated by weighted kappa between the ASIR and MRI and between the DLR and MRI. The confidence scores are expressed as the means and standard deviations.</p><p><strong>Results: </strong>Eighty-nine patients were enrolled, 52 in the HCC group (67 years ± 9 [mean ± SD], 46 men) and 37 in the non-HCC group (68 years ± 9, 33 men). The concordance coefficient between the LI-RADS classification by ASIR and MRI was 0.64 [0.52; 0.76], showing good agreement, that by DLR and MRI was 0.83 [0.73; 0.92], showing excellent agreement. The diagnostic confidence in ASIR was 3.31 ± 0.95 (mean ± SD) and 3.0 ± 1.11, that in the DLR was 3.9 ± 0.88 and 4.11 ± 0.75, that in the MRI was 4.46 ± 0.80 and 4.57 ± 0.80.</p><p><strong>Conclusion: </strong>DLR provided excellent LI-RADS classification concordance with MRI, whereas ASIR provided good concordance. The radiologists' confidence was greater in the DLR than in the ASIR but remained highest in the MR group.</p><p><strong>Key points: </strong>Question Does the use of deep learning reconstructions (DLR) improve LI-RADS classification of suspicious hepatocellular carcinoma lesions compared to adaptive statistical iterative reconstructions (ASIR)? Findings DLR demonstrated superior concordance of LI-RADS classification with MRI compared to ASIR. It also provided greater diagnostic confidence than ASIR. Clinical relevance The use of DLR enhances radiologists' ability to visualize and characterize lesions suspected of being HCC, as well as their LI-RADS classification. Moreover, it also boosts their confidence in interpreting these images.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4354-4361"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast feeding and contrast-enhanced imaging: the problem of missing data.","authors":"Sina Lemmenmeier, Aart J van der Molen, Ingrid B Böhm","doi":"10.1007/s00330-024-11201-9","DOIUrl":"10.1007/s00330-024-11201-9","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3948-3950"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The pericoronary fat attenuation index in cardio-oncology: a promising imaging biomarker and a need for standardization.","authors":"Costanza Lisi, Matthias Eberhard","doi":"10.1007/s00330-025-11481-9","DOIUrl":"10.1007/s00330-025-11481-9","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3895-3896"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining imaging guidelines for testicular germ-cell tumors: a commentary on the ESUR-SPIWG recommendations.","authors":"Shu-Huei Shen","doi":"10.1007/s00330-025-11560-x","DOIUrl":"10.1007/s00330-025-11560-x","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4055-4056"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.","authors":"Kai Ye, Libo Xu, Boyang Pan, Jie Li, Meijiao Li, Huishu Yuan, Nan-Jie Gong","doi":"10.1007/s00330-024-11317-y","DOIUrl":"10.1007/s00330-024-11317-y","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.</p><p><strong>Materials and methods: </strong>A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.</p><p><strong>Results: </strong>A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).</p><p><strong>Conclusion: </strong>DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.</p><p><strong>Key points: </strong>Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3739-3749"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic and prognostic value of quantitative cardiac magnetic resonance imaging biomarkers in systemic lupus erythematosus: a systematic review and meta-analysis.","authors":"Lévi-Dan Azoulay, Nadjia Kachenoura, Samia Boussouar, Etienne Charpentier, Alain Giron, Thomas Broussaud, Zahir Amoura, Alban Redheuil","doi":"10.1007/s00330-024-11282-6","DOIUrl":"10.1007/s00330-024-11282-6","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to compare CMR imaging biomarkers between SLE patients and matched controls.</p><p><strong>Materials and methods: </strong>Electronic databases were systematically searched from inception until November 2023. All studies reporting CMR imaging data in SLE patients were included. PRISMA guidelines were followed, and risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. CMR findings of SLE patients were compared to that of matched controls. Clinical features associated with CMR biomarkers were collected in a qualitative analysis.</p><p><strong>Results: </strong>A total of 64 studies were included in the systematic review pooling 3304 individuals including 1870 SLE patients. Of these, 19 case-control studies were included in the comparative meta-analysis (1576 individuals, including 884 SLE patients). Compared to controls, left ventricular (LV) ejection fraction (62% vs. 64%, p = 0.001) and indexed end-diastolic volume (77 vs. 72 mL/m², p = 0.006) were significantly altered in SLE patients. Late gadolinium enhancement (LGE) extent was higher in SLE patients (LGE mass/total LV mass: 3.5% vs. 1.1%, p = 0.009). Native T1 and T2 relaxation times were significantly higher in SLE patients (native T1 [1.5 T]: 1005 vs. 982 ms, p = 0.02; native T1 [3 T]: 1267 vs. 1140 ms, p < 0.001; T2 [all fields]: 58 vs. 51 ms, p < 0.001). Three studies found an association between disease activity and increased T2 relaxation times. Two studies identified an association between clinical outcomes and CMR parameters.</p><p><strong>Conclusions: </strong>While CMR-assessed ventricular function and volumes only slightly differed in SLE patients when compared to controls, myocardial tissue characterization parameters were significantly modified and associated with disease activity.</p><p><strong>Key points: </strong>Question What are the diagnostic and prognostic values of cardiac magnetic resonance (CMR) quantitative parameters in systemic lupus erythematosus (SLE) patients? Findings Myocardial tissue characterization parameters are significantly altered in SLE patients and associated with disease activity. Clinical relevance CMR imaging demonstrates subclinical cardiac alterations in systemic lupus erythematosus patients. Additional studies are required to further demonstrate the prognostic value of CMR in SLE.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3858-3870"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.","authors":"Gema Bruixola, Delfina Dualde-Beltrán, Ana Jimenez-Pastor, Anna Nogué, Fuensanta Bellvís, Almudena Fuster-Matanzo, Clara Alfaro-Cervelló, Nuria Grimalt, Nader Salhab-Ibáñez, Vicente Escorihuela, María Eugenia Iglesias, María Maroñas, Ángel Alberich-Bayarri, Andrés Cervantes, Noelia Tarazona","doi":"10.1007/s00330-024-11301-6","DOIUrl":"10.1007/s00330-024-11301-6","url":null,"abstract":"<p><strong>Background: </strong>Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk stratification.</p><p><strong>Methods: </strong>This single-centre observational study collected clinical data and baseline CT scans from 171 LAHNSCC patients treated with chemoradiation. The dataset was divided into training (80%) and test (20%) sets, with a 5-fold cross-validation on the training set. Researchers extracted 108 radiomics features from each primary tumour and applied survival analysis and classification models to predict progression-free survival (PFS) and 5-year progression, respectively. Performance was evaluated using inverse probability of censoring weights and c-index for the PFS model and AUC, sensitivity, specificity, and accuracy for the 5-year progression model. Feature importance was measured by the SHapley Additive exPlanations (SHAP) method and patient stratification was assessed through Kaplan-Meier curves.</p><p><strong>Results: </strong>The final dataset included 171 LAHNSCC patients, with 53% experiencing disease progression at 5 years. The random survival forest model best predicted PFS, with an AUC of 0.64 and CI of 0.66 on the test set, highlighting 4 radiomics features and TNM8 as significant contributors. It successfully stratified patients into low and high-risk groups (log-rank p < 0.005). The extreme gradient boosting model most effectively predicted a 5-year progression, incorporating 12 radiomics features and four clinical variables, achieving an AUC of 0.74, sensitivity of 0.53, specificity of 0.81, and accuracy of 0.66 on the test set.</p><p><strong>Conclusion: </strong>The combined clinical-radiomics model improved the standard TNM8 and clinical variables in predicting 5-year progression though further validation is necessary.</p><p><strong>Key points: </strong>Question There is an unmet need for non-invasive biomarkers to guide treatment in locally advanced head and neck cancer. Findings Clinical data (TNM8 staging, primary tumour site, age, and smoking) plus radiomics improved 5-year progression prediction compared with the clinical comprehensive model or TNM staging alone. Clinical relevance SHAP simplifies complex machine learning radiomics models for clinicians by using easy-to-understand graphical representations, promoting explainability.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"4277-4288"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancers not detected in one-view breast tomosynthesis screening-characteristics and reasons for non-detection.","authors":"Kristin Johnson, Debra M Ikeda, Ingvar Andersson, Sophia Zackrisson","doi":"10.1007/s00330-024-11278-2","DOIUrl":"10.1007/s00330-024-11278-2","url":null,"abstract":"<p><strong>Objectives: </strong>Limited understanding exists regarding non-detected cancers in digital breast tomosynthesis (DBT) screening. This study aims to classify non-detected cancers into true or false negatives, compare them with true positives, and analyze reasons for non-detection.</p><p><strong>Materials and methods: </strong>Conducted between 2010 and 2015, the prospective single-center Malmö Breast Tomosynthesis Screening Trial (MBTST) compared one-view DBT and two-view digital mammography (DM). Cancers not detected by DBT, i.e., interval cancers, those detected in the next screening round, and those only identified by DM, underwent a retrospective informed review by in total four breast radiologists. Reviewers classified cancers into true negative, false negative, or non-visible based on both DBT and DM findings and assessed radiographic appearances at screening and diagnosis, breast density, and reasons for non-detection. Statistics included the Pearson X² test.</p><p><strong>Results: </strong>In total, 89 cancers were not detected with DBT in the MBTST; eight cancers were solely in the DM reading mode, 59 during subsequent DM screening rounds, and 22 interval cancers. The proportion of cancers classified as false negative was 25% (22/89) based on DBT, compared with 18% (14/81) based on DM screening. The primary reason for false negatives was normal-appearing density, 50% (11/22). False negatives exhibited lower rates of high breast density, 36% (8/22), compared with true positives, 61% (78/129), p = 0.04, and spiculated densities were less frequent in false negatives, 41% (9/22) compared with true positives, 68% (88/129), p = 0.01.</p><p><strong>Conclusion: </strong>False negatives in one-view DBT screening commonly presented with spiculated features, but less frequently than true positives, and were missed or misinterpreted due to benign appearances.</p><p><strong>Key points: </strong>Question Cancers not detected in digital breast tomosynthesis screening, including false negatives, remain partly unexplored. Findings The most common reason behind false-negative cancers in a large screening trial was a normal-appearing density. Clinical relevance Recognizing the factors contributing to false negative findings in digital breast tomosynthesis screening is essential to further improve cancer detection.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3951-3960"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High altitudes, deeper insights: multicenter cardiovascular magnetic resonance study on hypertrophic cardiomyopathy.","authors":"Zixian Chen, Yue Sun, Na Yang, Jiang Nan, Likun Cao, Lei Zhao, Shengliang Liu, Jizhe Xu, Yuxi Li, Xiangui He, Yi Wu, Jian Gao, Zixuan Chen, Liang Cao, Yaping Zhang, Yanyu Li, Qi Xu, Shu Jiang, Jian Cao, Fangying Wei, Xiaojie Mao, Zhuoli Zhang, Yining Wang, Junqiang Lei","doi":"10.1007/s00330-024-11305-2","DOIUrl":"10.1007/s00330-024-11305-2","url":null,"abstract":"<p><strong>Objectives: </strong>Altitude is a known factor in cardiovascular disease, but its impact on hypertrophic cardiomyopathy (HCM) patients remains unclear. This study aimed to determine whether living at high altitudes affects the extent of late gadolinium enhancement (LGE) and left ventricular (LV) strain in HCM patients.</p><p><strong>Methods: </strong>This retrospective cross-sectional study was conducted across four hospitals located at different altitudes in China. A total of 256 HCM patients who underwent cardiac magnetic resonance (CMR) imaging between May 2019 and November 2021 were included. Patients were categorized into two groups: the high-altitude group (median interquartile range [IQR]: 1520.00 [1520.00, 1917.00] meters, n = 132) and the low-altitude group (86.45 [43.50, 150.75] meters, n = 124). The extent of LGE and global LV strain were assessed and compared between these groups.</p><p><strong>Results: </strong>The median age of the study population was 55 years (IQR: 46-63), with 59% of participants being male. The high-altitude group exhibited a significantly greater extent of LGE compared to the low-altitude group (median [IQR]: 8.10 [4.78, 19.98]% vs. 6.20 [1.89, 13.81]%; p = 0.008). Multivariable analysis identified altitude as an independent predictor of increased LGE extent (β = 4.41; 95% CI: 2.04 to 6.78; p < 0.001). Additionally, altitude was positively associated with LV strain in the longitudinal, circumferential, and radial directions (all p < 0.050).</p><p><strong>Conclusion: </strong>HCM patients living at higher altitudes exhibit a significant increase in LGE extent and more favorable LV strain parameters.</p><p><strong>Key points: </strong>Question Does altitude affect the extent of late gadolinium enhancement (LGE) and left ventricular strain in patients with hypertrophic cardiomyopathy (HCM)? Findings High altitude is associated with a significantly greater extent of LGE and less impairment in global longitudinal strain in HCM patients. Clinical relevance HCM patients living at higher altitudes exhibit a significant increase in LGE extent and the mismatch of left ventricular strains. Doctors should consider these findings to tailor treatment and follow-up plans for HCM patients living in high altitudes.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3883-3894"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invasive lobular carcinoma subgroup analysis of the MIPA study: real-world prospective data on preoperative MRI impact across the globe.","authors":"Lígia Pires-Gonçalves","doi":"10.1007/s00330-025-11414-6","DOIUrl":"10.1007/s00330-025-11414-6","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3987-3989"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}